import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('pubg_dataset.csv')
data.head()
data.info()
data.describe()
print("The average person kills",data['kills'].mean(),"players.")
print("99% of people have",np.percentile(data['kills'],99),"kills.")
print("The most kills ever recorded:",data['kills'].max())
data.columns
sns.distplot(data['matchDuration']);
print("Within 1250 to 1500, match duration is high.")
sns.distplot(data['walkDistance']);
plt.style.use('seaborn')
plt.figure()
plt.subplot(2,1,1)
plt.plot(data['matchDuration'],'-')
plt.subplot(2,1,2)
plt.plot(data['walkDistance'],'-');
plt.figure(figsize=(10,3))
plt.subplot(1,2,1)
plt.plot(data['matchDuration'],'-')
plt.subplot(1,2,2)
plt.plot(data['walkDistance'],'-');
sns.pairplot(data);
data['matchType'].value_counts()
sns.barplot(x='matchType',y='killPoints',data=data);
plt.xticks(rotation=70);
sns.barplot(x='matchType',y='weaponsAcquired',data=data);
plt.xticks(rotation=70);
data.select_dtypes(['category']).columns
sns.boxplot(x='matchType',y='winPlacePerc',data=data);
plt.xticks(rotation=70);
sns.boxplot(x='matchType',y='matchDuration',data=data);
plt.xticks(rotation=70);
sns.boxplot(x='matchDuration',y='matchType',data=data);
plt.xticks(rotation=70);
data['KILL'] = data['headshotKills'] + data['teamKills'] + data['roadKills']
data['KILL']
data['winPlacePerc'] = round(data['winPlacePerc'], 2)
data['winPlacePerc']
data_arr = []
for i in range(100):
data_arr.append(data['damageDealt'].sample(50).mean())
sns.distplot(data_arr);
plt.xlabel('damageDealt');